A phased optimization scheduling method for integrated energy systems incorporating industrial waste heat

Abstract

This invention discloses a phased optimization scheduling method for an integrated energy system incorporating industrial waste heat, comprising: forming an integrated energy system with electricity and industrial waste heat as inputs and outputs of cooling and heating energy; setting up energy flow mechanisms for the system during the summer cooling and winter heating phases; having neighboring enterprises act as sellers of industrial waste heat and the integrated energy system as buyers, realizing a master-slave game transaction of industrial waste heat between the two parties; during day-ahead scheduling, establishing a day-ahead optimization scheduling model for summer cooling and a day-ahead optimization scheduling model for winter heating, with the goal of optimizing system operating costs and energy utilization; during intraday scheduling, considering industrial waste heat transaction deviations, green electricity prediction deviations of photovoltaic generators, demand deviations of cooling and heating loads, and combining the demand response of system users, establishing an intraday optimization scheduling model for summer cooling and an intraday optimization scheduling model for winter heating, with the goal of minimizing adjustment costs.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system technology, specifically relating to a phased optimization scheduling method for an integrated energy system that incorporates industrial waste heat. Background Technology

[0002] Integrated energy systems are a new type of integrated energy system that combines multiple energy sources such as coal, natural gas, electricity, heat, and cooling. Through advanced physical information technology and innovative management models, it achieves coordinated and optimized energy scheduling. Typically, cooling load demand is higher during the summer cooling season and heating load demand is higher during the winter heating season. However, with the decreasing production and rising prices of traditional fossil fuels, as well as increasing environmental protection requirements, it is necessary to consider utilizing more economical, environmentally friendly, and energy-efficient energy sources to meet load demands.

[0003] Multiple industrial enterprises often exist near integrated energy systems, generating various types of industrial waste heat during their production processes. This waste heat is considered surplus and unused energy. Recycling and utilizing industrial waste heat is a key method for integrated energy systems to achieve energy conservation, emission reduction, and improved energy efficiency and economic viability. While industrial enterprises generate abundant waste heat resources across different sectors, its availability is limited by factors such as equipment operation, production plans, and market conditions. Therefore, how to integrate the industrial waste heat from neighboring enterprises into the integrated energy system, facilitate its rational trading, and comprehensively consider the instability of industrial waste heat output and trading prices with other renewable energy sources to achieve optimized scheduling of the integrated energy system is a pressing issue that needs to be addressed. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a phased optimization scheduling method for an integrated energy system that integrates industrial waste heat, so as to effectively utilize the industrial waste heat of nearby enterprises and realize the integrated energy system for summer cooling and winter heating.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] This invention provides a phased optimization scheduling method for an integrated energy system incorporating industrial waste heat, comprising:

[0007] S1. Install photovoltaic generator sets, electric chillers, absorption chillers, heat exchange equipment, cold storage devices, heat storage devices, electric storage devices, and industrial waste heat recovery units, and connect them to the municipal power grid to form a comprehensive energy system with electricity and industrial waste heat as inputs and outputs of cold and heat energy.

[0008] S2. Establish a phased energy flow mechanism for the integrated energy system, including:

[0009] Various types of industrial waste heat generated during the production and operation of nearby enterprises are input into the system's industrial waste heat recovery unit for recycling and treatment.

[0010] During the summer cooling season, the integrated energy system utilizes photovoltaic generators and municipal power grids to obtain electricity, which drives electric chillers for cooling. Simultaneously, it utilizes industrial waste heat recovery units to obtain a type of industrial waste heat as a heat source to drive absorption chillers for cooling. Additionally, it utilizes cold storage devices for cold energy storage and electrical energy storage devices for electrical energy storage.

[0011] During the winter heating season, the integrated energy system utilizes industrial waste heat recovery units to obtain various types of industrial waste heat, and outputs heat after heat exchange through heat exchange equipment; at the same time, it uses heat storage devices to store and release heat.

[0012] S3. Neighboring enterprises, acting as sellers of industrial waste heat, forecast the industrial waste heat generated during production and then submit their industrial waste heat supply and transaction prices to the integrated energy system, which acts as the buyer of industrial waste heat. The integrated energy system then provides feedback based on the demand values ​​of the system users' cooling and heating loads, the green electricity forecast values ​​of the photovoltaic generator sets, and the industrial waste heat declaration information submitted by the sellers, thereby realizing a master-slave game transaction of industrial waste heat between the two parties.

[0013] S4. During the day-ahead scheduling process, based on the results of the industrial waste heat transaction between the two parties, the integrated energy system establishes a day-ahead optimization scheduling model for summer cooling and a day-ahead optimization scheduling model for winter heating, with the goal of optimizing system operating costs and energy utilization, and obtains the operating output strategies of each equipment in the system during the summer cooling and winter heating phases of the day-ahead scheduling process.

[0014] S5. During intraday scheduling, considering the deviation of industrial waste heat trading, the green electricity prediction deviation of photovoltaic generators, the demand deviation of cooling load and heating load, and combined with the demand response of system users, with the goal of minimizing adjustment costs, establish intraday optimized scheduling models for summer cooling and winter heating of the system, and obtain the operating output strategies and demand response strategies of each equipment in the system during the summer cooling and winter heating phases of intraday scheduling.

[0015] Furthermore, in S1, the absorption chiller is a hot water absorption chiller; the heat exchange equipment includes a water-to-water heat exchanger and a steam-to-water heat exchanger.

[0016] The model of the hot water absorption chiller is represented as follows:

[0017] L AC (t)=η AC Q AC (t);

[0018] LAC,min ≤L AC (t)≤L AC,max ;

[0019] L AC (t) represents the cooling capacity generated by the hot water absorption chiller at time t; Q AC (t) represents the equivalent heat absorbed by the hot water absorption chiller from industrial waste heat at time t; η AC The coefficient of performance (COP) of a hot water absorption chiller; L AC,max L AC,min These are the upper and lower limits of the cooling capacity of hot water absorption cooling systems;

[0020] The model of the electric chiller is represented as follows:

[0021] L EC (t)=η EC E EC (t);

[0022] L EC,min ≤L EC (t)≤L EC,max ;

[0023] L EC (t) represents the cooling capacity generated by the electric chiller at time t; E EC 9t) represents the amount of electricity consumed by the electric chiller at time t; η EC L is the coefficient of performance (COP) of the electric chiller. EC,max L EC,min These are the upper and lower limits of the cooling capacity of an electric refrigeration unit, respectively.

[0024] The heat exchange device is represented by the following model:

[0025] W HE (t)=η HE S HE (t);

[0026] W HE (t) represents the heat generated by the heat exchanger at time t; S HE (t) represents the equivalent thermal power of the industrial waste heat input at time t; η HE The conversion efficiency of the heat exchange equipment.

[0027] Furthermore, in step S2, the various types of industrial waste heat generated during the production operation of nearby enterprises in the integrated energy system are input into the system's industrial waste heat recovery unit for recovery and treatment, including:

[0028] Establish corresponding waste heat transmission pipelines between nearby enterprises and integrated energy systems according to the different types of industrial waste heat, so as to transfer different types of industrial waste heat to the system's industrial waste heat recovery unit.

[0029] The industrial waste heat recovery unit monitors and adjusts the temperature, pressure, and flow parameters of different types of industrial waste heat to meet the operating parameters of absorption chillers for refrigeration and heat exchange equipment for heat exchange.

[0030] Furthermore, in S2, during the summer cooling phase, green electricity output from photovoltaic generators is prioritized. When green electricity is insufficient, electricity is purchased from the municipal power grid to drive the electric chiller for cooling. Simultaneously, the electric chiller serves as a supplementary cooling capacity device, used to consume electrical energy for cooling when the absorption chiller's cooling capacity is insufficient. Excess green electricity and / or low-cost electricity from the municipal power grid are stored in an energy storage device for release during peak cooling demand periods. When cooling supply exceeds demand, excess cooling capacity is stored in a cold storage device for release during peak cooling demand periods.

[0031] During the winter heating season, industrial waste heat of the corresponding type is obtained from the industrial waste heat recovery unit according to the type of heat exchange equipment, and the heat is output after heat exchange through the corresponding type of heat exchange equipment. At the same time, when the heat supply exceeds the demand, the excess heat is stored in the heat storage device for heat release during the peak heating period.

[0032] Furthermore, in step S3, the prediction of industrial waste heat generated during production operation includes:

[0033] Nearby enterprises obtain relevant data affecting industrial waste heat, including historical operating parameters of production equipment, raw material input, product output, type of industrial waste heat, waste heat temperature, waste heat flow rate, external weather conditions, changes in enterprise market demand, and historical waste heat.

[0034] Clustering algorithms are used to process the data, dividing the production operations of neighboring enterprises into multiple operating conditions;

[0035] Each dataset under each operating condition is used to extract features using multiple feature extraction algorithms, forming multiple data subsets for each operating condition.

[0036] For each data subset under each operating condition, multiple machine learning algorithms are used for training and learning to establish corresponding industrial waste heat prediction models. After evaluating the performance of each prediction model, the best-performing industrial waste heat prediction model under each operating condition is selected to obtain the predicted industrial waste heat values ​​for each operating condition of neighboring enterprises.

[0037] Furthermore, the clustering algorithm is the K-means clustering algorithm; the feature extraction algorithm includes the LASSO algorithm and the LightGBM algorithm; and the machine learning algorithm includes the CatBoost algorithm and the BPNN algorithm.

[0038] Furthermore, S3 includes:

[0039] Nearby enterprises, acting as sellers of industrial waste heat, use a pre-established industrial waste heat prediction model to forecast the industrial waste heat generated by the enterprises the following day, and take into account market factors, cost factors, and the preliminary purchase price given by the integrated energy system to initially formulate transaction prices for different types of industrial waste heat.

[0040] Nearby enterprises submit their applications to the integrated energy system for the supply and transaction prices of different types of industrial waste heat the following day.

[0041] During the summer cooling season: The integrated energy system uses a pre-established user cooling load forecasting model to obtain the next day's cooling load forecast, and a pre-established photovoltaic power generation forecasting model to obtain the next day's photovoltaic green power forecast. As the purchaser of industrial waste heat, the integrated energy system comprehensively considers the different types of industrial waste heat supply and transaction prices declared by nearby enterprises, the cooling load forecast, the photovoltaic green power forecast, and the grid dynamic price factors, and provides feedback to nearby enterprises on the different types of industrial waste heat purchase quantities and purchase prices. Nearby enterprises then adjust and optimize their declared transaction information based on the feedback information. After continuously executing the transaction process and making adjustments and optimizations, the final transaction quantities and transaction prices for different types of industrial waste heat are determined.

[0042] During the summer heating season: The integrated energy system uses a pre-established user heat load forecasting model to obtain the heat load forecast for the next day. After initially calculating the required industrial waste heat of different types, it comprehensively considers the industrial waste heat supply and transaction prices declared by nearby enterprises for different types, as well as the industrial waste heat demand for different types. Based on the industrial waste heat supply and demand relationship, it feeds back the purchase volume and purchase price of industrial waste heat of different types to nearby enterprises. Nearby enterprises then adjust and optimize their declared transaction information based on the feedback information. After continuously executing the transaction process and making adjustments and optimizations, the final transaction volume and transaction price of industrial waste heat of different types are determined.

[0043] The model involves establishing a master-slave game transaction model between the integrated energy system as the leader and nearby enterprises as followers, based on the optimal economic indicators of each party. Through multiple iterations, both parties continuously adjust their industrial waste heat trading volume and price information until they reach a Nash equilibrium point acceptable to both parties, thereby obtaining the optimal industrial waste heat trading volume and price strategy, as well as formulating the trading method.

[0044] Furthermore, in S4, with the goal of optimizing system operating costs and energy utilization, a pre-summer cooling day-end optimization scheduling model is established, expressed as:

[0045]

[0046] C h,buy,tThe cost of purchasing industrial waste heat for summer cooling; C e,buy,t The cost of purchasing electricity from the municipal power grid; C AC,t The operating cost of the absorption chiller; C EC,t The operating cost of the electric chiller; C PV,t The operating cost of the photovoltaic power generation unit; C s,t The energy storage and release operating cost of energy storage devices and cold storage devices; T is the dispatch cycle; L c,t E represents the cooling load generated by the system. h,e,t The amount of energy consumed for system cooling, including industrial waste heat and electricity consumed in cooling;

[0047] The day-ahead optimization scheduling model for winter heating, which aims to optimize system operating costs and energy utilization, is established as follows:

[0048]

[0049] C′ h,buy,t The cost of purchasing industrial waste heat for winter heating; C HE,t For the operating cost of heat exchange equipment; L h,t E represents the heat load generated by the system. h,t Waste heat from industry consumed by the system for heat generation;

[0050] The constraints of the system's summer cooling day-ahead optimization scheduling model include: operating constraints of the absorption chiller, operating constraints of the electric chiller, operating constraints of the photovoltaic generator, operating constraints of the energy storage device, operating constraints of the cold storage device, and cold power balance constraints.

[0051] The operating output strategies of each system device output by the pre-summer cooling day optimization scheduling model include: the cooling capacity output by the absorption chiller, the cooling capacity output by the electric chiller, the power generation of the photovoltaic generator, the electricity purchased by the municipal power grid, and the energy storage and release status and energy storage and release of the energy storage device and the cold storage device.

[0052] The constraints of the system’s winter heating day-ahead optimization scheduling model include: operating constraints of heat exchange equipment, operating constraints of heat storage devices, and heat power balance constraints.

[0053] The operating output strategies of each system device output by the system's pre-winter heating day-end optimization scheduling model include: the heat output of the heat exchange equipment, the heat storage and release status of the heat storage device, and the stored and released heat.

[0054] Furthermore, in S5, establishing the system's intraday optimal scheduling model for summer cooling specifically includes:

[0055] The daily forecast values ​​of industrial waste heat, cooling load, and photovoltaic power generation are obtained using a pre-established industrial waste heat prediction model, a pre-established user cooling load prediction model, and a pre-established photovoltaic power generation prediction model, respectively. These forecasts are then compared with the actual values ​​of industrial waste heat, cooling load, and photovoltaic power generation to analyze and obtain deviations in industrial waste heat trading, photovoltaic power generation green electricity prediction, and cooling load demand. When the combined deviation of these three deviations exceeds a pre-defined first deviation range, user-side cooling load demand response adjustment and equipment output adjustment are implemented to compensate for the deviation. With the goal of minimizing adjustment costs, a system summer cooling intraday optimization scheduling model is established, expressed as:

[0056]

[0057] C i,t C represents the cost of adjusting the operating output of the i-th device during summer cooling; c,DR,t This represents the cost of adjusting cooling load demand response on the user side during summer cooling season; M is the total number of absorption chillers, electric chillers, photovoltaic generators, energy storage devices, and cold storage devices.

[0058] The establishment of the system's intraday optimal scheduling model for winter heating specifically includes:

[0059] During the intraday scheduling of the system in the winter heating season: Intraday predicted values ​​of industrial waste heat are obtained using a pre-established industrial waste heat prediction model, and intraday predicted values ​​of heat load are obtained using a pre-established user heat load prediction model. These predicted and actual values ​​are then compared with the actual values ​​of industrial waste heat and heat load, respectively, to analyze and obtain the industrial waste heat trading deviation and the heat load demand deviation. When the combined deviation of the industrial waste heat trading deviation and the heat load demand deviation exceeds a set second deviation range, user-side heat load demand response adjustment and equipment operation output adjustment are implemented to compensate for the deviation. With the goal of minimizing adjustment costs, an intraday optimized scheduling model for the winter heating system is established, expressed as:

[0060]

[0061] C j,t The operating output adjustment cost of the j-th equipment during winter heating; C h,DR,t This represents the cost of adjusting the user's heat load demand response during winter heating; N is the total number of heat exchange equipment.

[0062] Furthermore, the Pelican optimization algorithm is used to solve the system's day-ahead optimization scheduling model for summer cooling, day-ahead optimization scheduling model for winter heating, intraday optimization scheduling model for summer cooling, and intraday optimization scheduling model for winter heating.

[0063] The beneficial effects of this invention are:

[0064] This invention, on the one hand, can utilize different types of industrial waste heat from nearby enterprises to meet the cooling load demand of the integrated energy system during the summer cooling phase and the heating load demand during the winter heating phase, thereby reducing fossil fuel consumption, improving the utilization rate of industrial waste heat, and enhancing the economic efficiency of nearby enterprises and the integrated energy system. On the other hand, considering the trading of industrial waste heat between nearby enterprises and the integrated energy system, a master-slave game model is established to realize the master-slave game trading of industrial waste heat between the two parties, ensuring the scientific rationality of the trading price and volume of industrial waste heat. Furthermore, with the goal of optimizing system operating costs and energy utilization, a system summer cooling and heating system is established. The day-ahead optimization scheduling models for seasonal cooling and winter heating are established to obtain day-ahead scheduling strategies. Taking into account various data deviations and load demand response, and aiming to minimize adjustment costs, intraday optimization scheduling models for summer cooling and winter heating are also established to obtain intraday scheduling strategies. This can improve the accuracy of scheduling strategies. At the same time, the modeling and prediction of industrial waste heat, green electricity from photovoltaic generators, and load demand instability in the system are carried out, and comprehensive deviation analysis is performed to achieve coordinated scheduling of various equipment and resources in the system, ensuring the economy, environmental protection, and supply and demand balance of the integrated energy system.

[0065] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0066] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0067] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0068] Figure 1 This is a flowchart of a phased optimization scheduling method for an integrated energy system incorporating industrial waste heat, according to the present invention.

[0069] Figure 2 This is a schematic diagram of the integrated energy system structure that incorporates industrial waste heat according to the present invention. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] Example 1

[0072] like Figure 1 , Figure 2 As shown, this embodiment 1 provides a phased optimization scheduling method for an integrated energy system incorporating industrial waste heat, including:

[0073] S1. Install photovoltaic generator sets, electric chillers, absorption chillers, heat exchange equipment, cold storage devices, heat storage devices, electric storage devices, and industrial waste heat recovery units, and connect them to the municipal power grid to form a comprehensive energy system with electricity and industrial waste heat as inputs and outputs of cold and heat energy.

[0074] S2. Establish a phased energy flow mechanism for the integrated energy system, including:

[0075] Various types of industrial waste heat generated during the production and operation of nearby enterprises are input into the system's industrial waste heat recovery unit for recycling and treatment.

[0076] During the summer cooling season, the integrated energy system utilizes photovoltaic generators and municipal power grids to obtain electricity, which drives electric chillers for cooling. Simultaneously, it utilizes industrial waste heat recovery units to obtain a type of industrial waste heat as a heat source to drive absorption chillers for cooling. Additionally, it utilizes cold storage devices for cold energy storage and electrical energy storage devices for electrical energy storage.

[0077] During the winter heating season, the integrated energy system utilizes industrial waste heat recovery units to obtain various types of industrial waste heat, and outputs heat after heat exchange through heat exchange equipment; at the same time, it uses heat storage devices to store and release heat.

[0078] S3. Neighboring enterprises, acting as sellers of industrial waste heat, forecast the industrial waste heat generated during production and then submit their industrial waste heat supply and transaction prices to the integrated energy system, which acts as the buyer of industrial waste heat. The integrated energy system then provides feedback based on the demand values ​​of the system users' cooling and heating loads, the green electricity forecast values ​​of the photovoltaic generator sets, and the industrial waste heat declaration information submitted by the sellers, thereby realizing a master-slave game transaction of industrial waste heat between the two parties.

[0079] S4. During the day-ahead scheduling process, based on the results of the industrial waste heat transaction between the two parties, the integrated energy system establishes a day-ahead optimization scheduling model for summer cooling and a day-ahead optimization scheduling model for winter heating, with the goal of optimizing system operating costs and energy utilization, and obtains the operating output strategies of each equipment in the system during the summer cooling and winter heating phases of the day-ahead scheduling process.

[0080] S5. During intraday scheduling, considering the deviation of industrial waste heat trading, the green electricity prediction deviation of photovoltaic generators, the demand deviation of cooling load and heating load, and combined with the demand response of system users, with the goal of minimizing adjustment costs, establish intraday optimized scheduling models for summer cooling and winter heating of the system, and obtain the operating output strategies and demand response strategies of each equipment in the system during the summer cooling and winter heating phases of intraday scheduling.

[0081] In this embodiment, in step S1, the absorption chiller is a hot water absorption chiller; the heat exchange equipment includes a water-to-water heat exchanger and a steam-to-water heat exchanger.

[0082] The model of the hot water absorption chiller is represented as follows:

[0083] L AC (t)=η AC Q AC (t);

[0084] L AC,min ≤L AC (t)≤L AC,max ;

[0085] L AC (t) represents the cooling capacity generated by the hot water absorption chiller at time t; Q AC 9t) represents the equivalent heat absorbed by the hot water absorption chiller from industrial waste heat at time t; η AC The coefficient of performance (COP) of a hot water absorption chiller; L AC,max L AC,min These are the upper and lower limits of the cooling capacity of hot water absorption cooling systems;

[0086] The model of the electric chiller is represented as follows:

[0087] L EC (t)=η EC E EC (t);

[0088] L EC,min ≤L EC (t)≤L EC,max ;

[0089] LEC (t) represents the cooling capacity generated by the electric chiller at time t; E EC 9t) represents the amount of electricity consumed by the electric chiller at time t; η EC L is the coefficient of performance (COP) of the electric chiller. EC,max L EC,min These are the upper and lower limits of the cooling capacity of an electric refrigeration unit, respectively.

[0090] The heat exchange device is represented by the following model:

[0091] W HE (t)=η HE S HE (t);

[0092] W HE (t) represents the heat generated by the heat exchanger at time t; S HE 9t) represents the equivalent thermal power of the industrial waste heat input at time t; η HE The conversion efficiency of the heat exchange equipment.

[0093] It should be noted that electric chillers, as auxiliary cooling equipment in integrated energy systems, meet the additional cooling demand when absorption chillers driven by industrial waste heat cannot meet the cooling energy requirements. The working principle of an electric chiller is that electricity drives a compressor, causing the internal refrigerant to liquefy and release heat, and then heat transfer occurs through evaporation and heat absorption. Absorption chillers, as the main component for industrial waste heat utilization and a key device for the system's energy cascade utilization, typically use lithium bromide as the working fluid. Hot water type lithium bromide absorption chillers are the most widely used. The heat source for absorption chillers is industrial waste heat generated during the production operation of nearby enterprises, and cooling is achieved through changes in the physical properties of the internal working fluid. Nearby enterprises refer to industrial production enterprises, such as steel, chemical, and building materials companies, which are the main sources of industrial waste heat. The heat sources for industrial waste heat are diverse, including steam, flue gas, and hot water.

[0094] In this application, the number of heat exchangers is at least two, and they are of two different types, capable of utilizing different types of industrial waste heat. Additionally, the number of electric chillers is also multiple, ensuring sufficient additional cooling capacity when industrial waste heat is insufficient. Multiple photovoltaic generator sets are also included, providing more green electricity to the integrated energy system, meeting users' daily electricity needs, and reducing the cost of purchasing electricity from the municipal grid and carbon emissions. Overall, through phased energy management and utilization for summer cooling and winter heating, the cascade utilization of industrial waste heat and the rational allocation of electricity can be achieved, forming an integrated energy system that uses electricity and industrial waste heat as input to output cooling capacity during the summer cooling phase, and an integrated energy system that uses industrial waste heat as input to output heat capacity during the winter heating phase.

[0095] Steam-water heat exchangers can transfer the heat contained in steam to hot water, thus realizing the conversion of waste heat between different media. Water-water heat exchangers are devices that exchange heat between two water streams at different temperatures. Of course, depending on the type of industrial waste heat, there can also be flue gas-water heat exchangers, which exchange the heat contained in high-temperature flue gas to hot water.

[0096] The model of a photovoltaic power generation unit is represented as follows:

[0097]

[0098] P pv P represents the output power of the photovoltaic generator set. pv,s G is the rated power of the photovoltaic generator set. C G represents the actual radiation intensity of the light; STC Light radiation intensity under standard conditions; k is the power temperature coefficient; T c This refers to the actual temperature of the photovoltaic panel; T STC The temperature of the photovoltaic panel under standard conditions;

[0099] Thermal storage devices, electrical storage devices, and cold storage devices have similar models, differing only in the type of energy. Taking cold storage devices as an example, the model is represented as follows:

[0100]

[0101] C CESS 9t) represents the stored cold energy after the cold storage device has stored and released cold energy; C CESS (t-1) represents the amount of cold storage at the end of time period (t-1); δ c The loss rate of the cold storage device; These are the conversion efficiencies of the cold energy released and the cold energy stored in the cold storage device, respectively. The input cooling power of the cold storage device during time period t; Δt represents the output cooling power of the cold storage device during time period t; Δt represents the unit scheduling time. These are the upper and lower limits for cold storage devices, respectively.

[0102] In this embodiment, step S2 involves inputting various types of industrial waste heat generated during the production operation of nearby enterprises into the system's industrial waste heat recovery unit for recycling, including:

[0103] Establish corresponding waste heat transmission pipelines between nearby enterprises and integrated energy systems according to the different types of industrial waste heat, so as to transfer different types of industrial waste heat to the system's industrial waste heat recovery unit.

[0104] The industrial waste heat recovery unit monitors and adjusts the temperature, pressure, and flow parameters of different types of industrial waste heat to meet the operating parameters of absorption chillers for refrigeration and heat exchange equipment for heat exchange.

[0105] In this embodiment, during the summer cooling phase, in step S2, green electricity output from photovoltaic generators is used preferentially. When green electricity is insufficient, electricity is purchased from the municipal power grid to drive the electric chiller for cooling. Simultaneously, the electric chiller serves as a supplementary cooling capacity device, used to consume electrical energy for cooling when the absorption chiller's cooling capacity is insufficient. Furthermore, excess green electricity and / or low-priced electricity from the municipal power grid are stored in an energy storage device for release during peak cooling demand periods. When cooling supply exceeds demand, excess cooling capacity is stored in a cold storage device for release during peak cooling demand periods.

[0106] During the winter heating season, industrial waste heat of the corresponding type is obtained from the industrial waste heat recovery unit according to the type of heat exchange equipment, and the heat is output after heat exchange through the corresponding type of heat exchange equipment. At the same time, when the heat supply exceeds the demand, the excess heat is stored in the heat storage device for heat release during the peak heating period.

[0107] In this embodiment, step S3, predicting the industrial waste heat generated during production operation, includes:

[0108] Nearby enterprises obtain relevant data affecting industrial waste heat, including historical operating parameters of production equipment, raw material input, product output, type of industrial waste heat, waste heat temperature, waste heat flow rate, external weather conditions, changes in enterprise market demand, and historical waste heat.

[0109] Clustering algorithms are used to process the data, dividing the production operations of neighboring enterprises into multiple operating conditions;

[0110] Each dataset under each operating condition is used to extract features using multiple feature extraction algorithms, forming multiple data subsets for each operating condition.

[0111] For each data subset under each operating condition, multiple machine learning algorithms are used for training and learning to establish corresponding industrial waste heat prediction models. After evaluating the performance of each prediction model, the best-performing industrial waste heat prediction model under each operating condition is selected to obtain the predicted industrial waste heat values ​​for each operating condition of neighboring enterprises.

[0112] In practical applications, the industrial waste heat of neighboring enterprises is affected by various factors. Some industrial enterprises operate 24 hours a day, but during nighttime, holidays, production scheduling, equipment maintenance, etc., production time and output will be reduced, or even short-term shutdowns will occur. These factors will cause instability in industrial waste heat during a certain period. Moreover, the production operation of different enterprises is also related to enterprise size, production process, market conditions, scheduling plans, and the off-season and peak season of enterprise output, which will directly affect the output of industrial waste heat. In order to consider the different types of industrial waste heat of neighboring enterprises, the production operation of enterprises can be divided into multiple operating conditions. Each operating condition represents the actual production operation and industrial waste heat output of the enterprise. The factors affecting industrial waste heat will be different under each actual operating condition.

[0113] In this embodiment, the clustering algorithm is the K-means clustering algorithm; the feature extraction algorithm includes the LASSO algorithm and the LightGBM algorithm; and the machine learning algorithm includes the CatBoost algorithm and the BPNN algorithm.

[0114] For each operating condition, the dataset is divided into two parts, such as dataset 1 and dataset 2. The LASSO algorithm is used for dataset 1, and the LightGBM algorithm is used for dataset 2. Data features are filtered, and data with higher importance is selected for prediction, thereby reducing data dimensionality and improving the accuracy of model prediction. Then, the CatBoost algorithm and BPNN algorithm are used for training and learning on dataset 1, and the same algorithm is used for training and learning on dataset 2. Industrial waste heat prediction models for each algorithm are established for the corresponding datasets. Finally, the root mean square error of the prediction models is evaluated, and the optimal prediction model for each operating condition is selected for predicting industrial waste heat.

[0115] In this embodiment, S3 includes:

[0116] Nearby enterprises, acting as sellers of industrial waste heat, use a pre-established industrial waste heat prediction model to forecast the industrial waste heat generated by the enterprises the following day, and take into account market factors, cost factors, and the preliminary purchase price given by the integrated energy system to initially formulate transaction prices for different types of industrial waste heat.

[0117] Nearby enterprises submit their applications to the integrated energy system for the supply and transaction prices of different types of industrial waste heat the following day.

[0118] During the summer cooling season: The integrated energy system uses a pre-established user cooling load forecasting model to obtain the next day's cooling load forecast, and a pre-established photovoltaic power generation forecasting model to obtain the next day's photovoltaic green power forecast. As the purchaser of industrial waste heat, the integrated energy system comprehensively considers the different types of industrial waste heat supply and transaction prices declared by nearby enterprises, the cooling load forecast, the photovoltaic green power forecast, and the grid dynamic price factors, and provides feedback to nearby enterprises on the different types of industrial waste heat purchase quantities and purchase prices. Nearby enterprises then adjust and optimize their declared transaction information based on the feedback information. After continuously executing the transaction process and making adjustments and optimizations, the final transaction quantities and transaction prices for different types of industrial waste heat are determined.

[0119] During the summer heating season: The integrated energy system uses a pre-established user heat load forecasting model to obtain the heat load forecast for the next day. After initially calculating the required industrial waste heat of different types, it comprehensively considers the industrial waste heat supply and transaction prices declared by nearby enterprises for different types, as well as the industrial waste heat demand for different types. Based on the industrial waste heat supply and demand relationship, it feeds back the purchase volume and purchase price of industrial waste heat of different types to nearby enterprises. Nearby enterprises then adjust and optimize their declared transaction information based on the feedback information. After continuously executing the transaction process and making adjustments and optimizations, the final transaction volume and transaction price of industrial waste heat of different types are determined.

[0120] The model involves establishing a master-slave game transaction model between the integrated energy system as the leader and nearby enterprises as followers, based on the optimal economic indicators of each party. Through multiple iterations, both parties continuously adjust their industrial waste heat trading volume and price information until they reach a Nash equilibrium point acceptable to both parties, thereby obtaining the optimal industrial waste heat trading volume and price strategy, as well as formulating the trading method.

[0121] In practical applications, the transaction process between the integrated energy system and neighboring enterprises can be viewed as a master-slave game. The upper-level leader in the master-slave game is the integrated energy system, and the lower-level followers are the neighboring enterprises. The transaction information provided by the integrated energy system will affect the industrial waste heat supply of the neighboring enterprises, while the industrial waste heat transaction information declared by the neighboring enterprises will affect the integrated energy system's decision to purchase industrial waste heat. The two eventually reach an equilibrium solution in the game process.

[0122] As a leader in integrated energy systems, the following factors are primarily considered:

[0123] 1) Demand-driven: Integrated energy systems are responsible for providing heating and cooling load services to users; therefore, their demand for industrial waste heat is the dominant factor. Integrated energy systems can determine the amount of industrial waste heat needed based on user demand, the availability of other energy sources (such as solar power, grid electricity, etc.), and the status of the energy storage system.

[0124] 2) Market Influence: Integrated energy systems typically possess significant market influence, capable of impacting the energy supply landscape of an entire region. Therefore, they have the ability to set certain price ranges and trading conditions.

[0125] 3) Multi-energy synergy: Integrated energy systems need to coordinate multiple energy sources (such as industrial waste heat, photovoltaic power generation, grid power, etc.), so they need a global perspective to optimize energy allocation, thereby ensuring the economy and stability of system operation.

[0126] Neighboring companies, as followers, primarily consider the following factors:

[0127] 1) Supply Response: Nearby enterprises are primarily responsible for supplying industrial waste heat. They need to adjust their supply strategies based on the needs of the integrated energy system and the proposed trading terms.

[0128] 2) Costs and Benefits: Nearby businesses need to consider the costs of waste heat generated during their production processes and the potential revenue from trading. They will decide whether to accept the trade and how much waste heat to provide based on the integrated energy system's quote.

[0129] 3) Flexibility: Neighboring businesses typically have a certain degree of flexibility to adjust their production plans, thereby affecting the amount of waste heat generated. However, such adjustments must be made without disrupting normal production.

[0130] In the master-slave game trading model, the objective function of the upper-level integrated energy system is to minimize operating costs, including the cost of purchasing electricity from the municipal power grid, the cost of purchasing industrial waste heat, and the operating costs of various equipment. The objective function of the lower-level neighboring enterprises is to maximize profits, i.e., the revenue obtained from selling industrial waste heat minus the related costs of collecting and treating industrial waste heat. Through the master-slave game trading model, the integrated energy system and neighboring enterprises can jointly optimize the industrial waste heat trading process and achieve a win-win situation.

[0131] The master-slave game trading model is represented as follows:

[0132] G = {AE,IES,ρ} price,t ,L wh,t ,F AE ,F IES};

[0133] AE and IES are participants in a master-slave game, representing neighboring enterprises and integrated energy systems; ρ price,t The price of industrial waste heat at time t; L wh,t F represents the industrial waste heat trading volume at time t. AE F IES These are the objective functions of neighboring firms in a master-slave game and the objective function of the integrated energy system, respectively.

[0134] In a master-slave game transaction model, if the solutions and equilibrium solutions of the upper and lower level models are respectively (ρ price,t ,L wh,t ), (ρ * price,t ,L * wh,t The game reaches equilibrium when the following conditions are met:

[0135]

[0136] In this embodiment, in step S4, with the goal of optimizing system operating costs and energy utilization, an optimized scheduling model for the summer cooling season is established, expressed as:

[0137]

[0138] C h,buy,t The cost of purchasing industrial waste heat for summer cooling; C e,buy,t The cost of purchasing electricity from the municipal power grid; C AC,t The operating cost of the absorption chiller; C EC,t The operating cost of the electric chiller; C PV,t The operating cost of the photovoltaic power generation unit; C s,t The energy storage and release operating cost of energy storage devices and cold storage devices; T is the dispatch cycle; L c,t E represents the cooling load generated by the system. h,e,t The amount of energy consumed for system cooling, including industrial waste heat and electricity consumed in cooling;

[0139] The day-ahead optimization scheduling model for winter heating, which aims to optimize system operating costs and energy utilization, is established as follows:

[0140]

[0141] C′ h,buy,t The cost of purchasing industrial waste heat for winter heating; C HE,t For the operating cost of heat exchange equipment; L h,t E represents the heat load generated by the system. h,t Waste heat from industry consumed by the system for heat generation;

[0142] The constraints of the system's summer cooling day-ahead optimization scheduling model include: operating constraints of the absorption chiller, operating constraints of the electric chiller, operating constraints of the photovoltaic generator, operating constraints of the energy storage device, operating constraints of the cold storage device, and cold power balance constraints.

[0143] The operating output strategies of each system device output by the pre-summer cooling day optimization scheduling model include: the cooling capacity output by the absorption chiller, the cooling capacity output by the electric chiller, the power generation of the photovoltaic generator, the electricity purchased by the municipal power grid, and the energy storage and release status and energy storage and release of the energy storage device and the cold storage device.

[0144] The constraints of the system's winter heating day-ahead optimization scheduling model include: operating constraints of heat exchange equipment, operating constraints of heat storage devices, and heat power balance constraints.

[0145] The operating output strategies of each system device output by the system's pre-winter heating day-end optimization scheduling model include: the heat output of the heat exchange equipment, the heat storage and release status of the heat storage device, and the stored and released heat.

[0146] In this embodiment, step S5, establishing the system's intraday optimal scheduling model for summer cooling specifically includes:

[0147] The daily forecast values ​​of industrial waste heat, cooling load, and photovoltaic power generation are obtained using a pre-established industrial waste heat prediction model, a pre-established user cooling load prediction model, and a pre-established photovoltaic power generation prediction model, respectively. These forecasts are then compared with the actual values ​​of industrial waste heat, cooling load, and photovoltaic power generation to analyze and obtain deviations in industrial waste heat trading, photovoltaic power generation green electricity prediction, and cooling load demand. When the combined deviation of these three deviations exceeds a pre-defined first deviation range, user-side cooling load demand response adjustment and equipment output adjustment are implemented to compensate for the deviation. With the goal of minimizing adjustment costs, a system summer cooling intraday optimization scheduling model is established, expressed as:

[0148]

[0149] C i,t C represents the cost of adjusting the operating output of the i-th device during summer cooling; c,DR,t This represents the cost of adjusting cooling load demand response on the user side during summer cooling season; M is the total number of absorption chillers, electric chillers, photovoltaic generators, energy storage devices, and cold storage devices.

[0150] The establishment of the system's intraday optimal scheduling model for winter heating specifically includes:

[0151] During the intraday scheduling of the system in the winter heating season: Intraday predicted values ​​of industrial waste heat are obtained using a pre-established industrial waste heat prediction model, and intraday predicted values ​​of heat load are obtained using a pre-established user heat load prediction model. These predicted and actual values ​​are then compared with the actual values ​​of industrial waste heat and heat load, respectively, to analyze and obtain the industrial waste heat trading deviation and the heat load demand deviation. When the combined deviation of the industrial waste heat trading deviation and the heat load demand deviation exceeds a set second deviation range, user-side heat load demand response adjustment and equipment operation output adjustment are implemented to compensate for the deviation. With the goal of minimizing adjustment costs, an intraday optimized scheduling model for the winter heating system is established, expressed as:

[0152]

[0153] C j,t The operating output adjustment cost of the j-th equipment during winter heating; C h,DR,t This represents the cost of adjusting the user's heat load demand response during winter heating; N is the total number of heat exchange equipment.

[0154] In this embodiment, the Pelican optimization algorithm is used to solve the system's summer cooling day-ahead optimization scheduling model, winter heating day-ahead optimization scheduling model, summer cooling day-ahead optimization scheduling model, and winter heating day-ahead optimization scheduling model.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0156] Furthermore, the functional modules in the various embodiments of this invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. If the function is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0157] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A phased optimization scheduling method for an integrated energy system incorporating industrial waste heat, characterized in that, include: S1. Install photovoltaic generator sets, electric chillers, absorption chillers, heat exchange equipment, cold storage devices, heat storage devices, electric storage devices, and industrial waste heat recovery units, and connect them to the municipal power grid to form a comprehensive energy system with electricity and industrial waste heat as inputs and outputs of cold and heat energy. S2. Establish a phased energy flow mechanism for the integrated energy system, including: Various types of industrial waste heat generated during the production and operation of nearby enterprises are input into the system's industrial waste heat recovery unit for recycling and treatment. During the summer cooling season, the integrated energy system utilizes photovoltaic generators and municipal power grids to obtain electricity, which drives electric chillers for cooling. Simultaneously, it utilizes industrial waste heat recovery units to obtain a type of industrial waste heat as a heat source to drive absorption chillers for cooling. Additionally, it utilizes cold storage devices for cold energy storage and electrical energy storage devices for electrical energy storage. During the winter heating season, the integrated energy system utilizes industrial waste heat recovery units to obtain various types of industrial waste heat, and outputs heat after heat exchange through heat exchange equipment; at the same time, it uses heat storage devices to store and release heat. S3. Neighboring enterprises, acting as sellers of industrial waste heat, forecast the industrial waste heat generated during production and then submit their industrial waste heat supply and transaction prices to the integrated energy system, which acts as the buyer of industrial waste heat. The integrated energy system then provides feedback based on the demand values ​​of the system users' cooling and heating loads, the green electricity forecast values ​​of the photovoltaic generator sets, and the industrial waste heat declaration information submitted by the sellers, thereby realizing a master-slave game transaction of industrial waste heat between the two parties. S4. During the day-ahead scheduling process, based on the results of the industrial waste heat transaction between the two parties, the integrated energy system establishes a day-ahead optimization scheduling model for summer cooling and a day-ahead optimization scheduling model for winter heating, with the goal of optimizing system operating costs and energy utilization, and obtains the operating output strategies of each equipment in the system during the summer cooling and winter heating phases of the day-ahead scheduling process. S5. During intraday scheduling, considering the deviation of industrial waste heat trading, the green electricity prediction deviation of photovoltaic generators, the demand deviation of cooling load and heating load, and combined with the demand response of system users, with the goal of minimizing adjustment costs, establish intraday optimized scheduling models for summer cooling and winter heating of the system, and obtain the operating output strategies and demand response strategies of each equipment in the system during the summer cooling and winter heating phases of intraday scheduling.

2. The phased optimization scheduling method for integrated energy systems according to claim 1, characterized in that, In S1, the absorption chiller is a hot water absorption chiller; the heat exchange equipment includes a water-to-water heat exchanger and a steam-to-water heat exchanger. The model of the hot water absorption chiller is represented as follows: L AC (t AC Q AC (t); L AC,min ≤L AC (t)≤L AC,max ; L AC (t) represents the cooling capacity generated by the hot water absorption chiller at time t; Q AC (t) represents the equivalent heat absorbed by the hot water absorption chiller from industrial waste heat at time t; η AC The coefficient of performance (COP) of a hot water absorption chiller; L AC,max L AC,min These are the upper and lower limits of the cooling capacity of hot water absorption cooling systems; The model of the electric chiller is represented as follows: L EC (t)=η EC AND EC (t)? L EC,min ≤L EC (t)≤L EC,max ; L EC (t) represents the cooling capacity generated by the electric chiller at time t; E EC (t) represents the amount of electricity consumed by the electric chiller at time t; η EC L is the coefficient of performance (COP) of the electric chiller. EC,max L EC,min These are the upper and lower limits of the cooling capacity of an electric refrigeration unit, respectively. The heat exchange device is represented by the following model: W HE (t)=η HE S HE (t); W HE (t) represents the heat generated by the heat exchanger at time t; S HE (t) represents the equivalent thermal power of the industrial waste heat input at time t; η HE The conversion efficiency of the heat exchange equipment.

3. The phased optimization scheduling method for integrated energy systems according to claim 1, characterized in that, In step S2, various types of industrial waste heat generated during the production operation of nearby enterprises in the integrated energy system are input into the system's industrial waste heat recovery unit for recovery and treatment, including: Establish corresponding waste heat transmission pipelines between nearby enterprises and integrated energy systems according to the different types of industrial waste heat, so as to transfer different types of industrial waste heat to the system's industrial waste heat recovery unit. The industrial waste heat recovery unit monitors and adjusts the temperature, pressure, and flow parameters of different types of industrial waste heat to meet the operating parameters of absorption chillers for refrigeration and heat exchange equipment for heat exchange.

4. The phased optimization scheduling method for integrated energy systems according to claim 1, characterized in that, In S2, during the summer cooling season, green electricity output from photovoltaic generators is used first. When green electricity is insufficient, electricity is purchased from the municipal power grid to drive electric chillers for cooling. Simultaneously, electric chillers serve as supplementary cooling equipment, consuming electrical energy for cooling when the absorption chiller's cooling capacity is insufficient. Furthermore, excess green electricity and / or low-priced electricity from the municipal power grid are stored in energy storage devices for release during peak cooling demand periods. When cooling supply exceeds demand, excess cooling capacity is stored in cold storage devices for release during peak cooling demand periods. During the winter heating season, industrial waste heat of the corresponding type is obtained from the industrial waste heat recovery unit according to the type of heat exchange equipment, and the heat is output after heat exchange through the corresponding type of heat exchange equipment. At the same time, when the heat supply exceeds the demand, the excess heat is stored in the heat storage device for heat release during the peak heating period.

5. The phased optimization scheduling method for integrated energy systems according to claim 1, characterized in that, In step S3, the prediction of industrial waste heat generated during production operation includes: Nearby enterprises obtain relevant data affecting industrial waste heat, including historical operating parameters of production equipment, raw material input, product output, type of industrial waste heat, waste heat temperature, waste heat flow rate, external weather conditions, changes in enterprise market demand, and historical waste heat. Clustering algorithms are used to process the data, dividing the production operations of neighboring enterprises into multiple operating conditions; Each dataset under each operating condition is used to extract features using multiple feature extraction algorithms, forming multiple data subsets for each operating condition. For each data subset under each operating condition, multiple machine learning algorithms are used for training and learning to establish corresponding industrial waste heat prediction models. After evaluating the performance of each prediction model, the best-performing industrial waste heat prediction model under each operating condition is selected to obtain the predicted industrial waste heat values ​​for each operating condition of neighboring enterprises.

6. The phased optimization scheduling method for integrated energy systems according to claim 5, characterized in that, The clustering algorithm is the K-means clustering algorithm; the feature extraction algorithm includes the LASSO algorithm and the LightGBM algorithm; the machine learning algorithm includes the CatBoost algorithm and the BPNN algorithm.

7. The phased optimization scheduling method for integrated energy systems according to claim 1, characterized in that, S3 includes: Nearby enterprises, acting as sellers of industrial waste heat, use a pre-established industrial waste heat prediction model to forecast the industrial waste heat generated by the enterprises the following day, and take into account market factors, cost factors, and the preliminary purchase price given by the integrated energy system to initially formulate transaction prices for different types of industrial waste heat. Nearby enterprises submit their applications to the integrated energy system for the supply and transaction prices of different types of industrial waste heat the following day. During the summer cooling season: The integrated energy system uses a pre-established user cooling load forecasting model to obtain the next day's cooling load forecast, and a pre-established photovoltaic power generation forecasting model to obtain the next day's photovoltaic green power forecast. As the purchaser of industrial waste heat, the integrated energy system comprehensively considers the different types of industrial waste heat supply and transaction prices declared by nearby enterprises, the cooling load forecast, the photovoltaic green power forecast, and the grid dynamic price factors, and provides feedback to nearby enterprises on the different types of industrial waste heat purchase quantities and purchase prices. Nearby enterprises then adjust and optimize their declared transaction information based on the feedback information. After continuously executing the transaction process and making adjustments and optimizations, the final transaction quantities and transaction prices for different types of industrial waste heat are determined. During the summer heating season: The integrated energy system uses a pre-established user heat load forecasting model to obtain the heat load forecast for the next day. After initially calculating the required industrial waste heat of different types, it comprehensively considers the industrial waste heat supply and transaction prices declared by nearby enterprises for different types, as well as the industrial waste heat demand for different types. Based on the industrial waste heat supply and demand relationship, it feeds back the purchase volume and purchase price of industrial waste heat of different types to nearby enterprises. Nearby enterprises then adjust and optimize their declared transaction information based on the feedback information. After continuously executing the transaction process and making adjustments and optimizations, the final transaction volume and transaction price of industrial waste heat of different types are determined. The model involves establishing a master-slave game transaction model between the integrated energy system as the leader and nearby enterprises as followers, based on the optimal economic indicators of each party. Through multiple iterations, both parties continuously adjust their industrial waste heat trading volume and price information until they reach a Nash equilibrium point acceptable to both parties, thereby obtaining the optimal industrial waste heat trading volume and price strategy, as well as formulating the trading method.

8. The phased optimization scheduling method for integrated energy systems according to claim 1, characterized in that, In S4, with the goal of optimizing system operating costs and energy utilization, an optimal scheduling model for the summer cooling season is established, expressed as follows: C h,buy,t The cost of purchasing industrial waste heat for summer cooling; C e,buy,t The cost of purchasing electricity from the municipal power grid; C AC,t The operating cost of the absorption chiller; C EC,t The operating cost of the electric chiller; C PV,t The operating cost of the photovoltaic power generation unit; C s,t The energy storage and release operating cost of energy storage devices and cold storage devices; T is the dispatch cycle; L c,t E represents the cooling load generated by the system. h,e,t The amount of energy consumed for system cooling, including industrial waste heat and electricity consumed in cooling; The day-ahead optimization scheduling model for winter heating, which aims to optimize system operating costs and energy utilization, is established as follows: C h ′ ,buy,t The cost of purchasing industrial waste heat for winter heating; C HE,t For the operating cost of heat exchange equipment; L h,t E represents the heat load generated by the system. h,t Waste heat from industry consumed by the system for heat generation; The constraints of the system's summer cooling day-ahead optimization scheduling model include: operating constraints of the absorption chiller, operating constraints of the electric chiller, operating constraints of the photovoltaic generator, operating constraints of the energy storage device, operating constraints of the cold storage device, and cold power balance constraints. The operating output strategies of each system device output by the pre-summer cooling day optimization scheduling model include: the cooling capacity output by the absorption chiller, the cooling capacity output by the electric chiller, the power generation of the photovoltaic generator, the electricity purchased by the municipal power grid, and the energy storage and release status and energy storage and release of the energy storage device and the cold storage device. The constraints of the system’s winter heating day-ahead optimization scheduling model include: operating constraints of heat exchange equipment, operating constraints of heat storage devices, and heat power balance constraints. The operating output strategies of each system device output by the system's pre-winter heating day-end optimization scheduling model include: the heat output of the heat exchange equipment, the heat storage and release status of the heat storage device, and the stored and released heat.

9. The phased optimization scheduling method for integrated energy systems according to claim 1, characterized in that, In S5, establishing the intraday optimal scheduling model for summer cooling supply specifically includes: The daily forecast values ​​of industrial waste heat, cooling load, and photovoltaic power generation are obtained using a pre-established industrial waste heat prediction model, a pre-established user cooling load prediction model, and a pre-established photovoltaic power generation prediction model, respectively. These forecasts are then compared with the actual values ​​of industrial waste heat, cooling load, and photovoltaic power generation to analyze and obtain deviations in industrial waste heat trading, photovoltaic power generation green electricity prediction, and cooling load demand. When the combined deviation of these three deviations exceeds a pre-defined first deviation range, user-side cooling load demand response adjustment and equipment output adjustment are implemented to compensate for the deviation. With the goal of minimizing adjustment costs, a system summer cooling intraday optimization scheduling model is established, expressed as: C i,t C represents the cost of adjusting the operating output of the i-th device during summer cooling; c,DR,t This represents the cost of adjusting cooling load demand response on the user side during summer cooling season; M is the total number of absorption chillers, electric chillers, photovoltaic generators, energy storage devices, and cold storage devices. The establishment of the system's intraday optimal scheduling model for winter heating specifically includes: During the intraday scheduling of the system in the winter heating season: Intraday predicted values ​​of industrial waste heat are obtained using a pre-established industrial waste heat prediction model, and intraday predicted values ​​of heat load are obtained using a pre-established user heat load prediction model. These predicted and actual values ​​are then compared with the actual values ​​of industrial waste heat and heat load, respectively, to analyze and obtain the industrial waste heat trading deviation and the heat load demand deviation. When the combined deviation of the industrial waste heat trading deviation and the heat load demand deviation exceeds a set second deviation range, user-side heat load demand response adjustment and equipment operation output adjustment are implemented to compensate for the deviation. With the goal of minimizing adjustment costs, an intraday optimized scheduling model for the winter heating system is established, expressed as: C j,t The operating output adjustment cost of the j-th equipment during winter heating; C h,DR,t This represents the cost of adjusting the user's heat load demand response during winter heating; N is the total number of heat exchange equipment.

10. The phased optimization scheduling method for integrated energy systems according to claim 1, characterized in that, The Pelican optimization algorithm is used to solve the system's day-ahead optimization scheduling model for summer cooling, day-ahead optimization scheduling model for winter heating, intraday optimization scheduling model for summer cooling, and intraday optimization scheduling model for winter heating.

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